In the medical field, with advance in diagnostic imaging apparatus, such as X-ray CT, MRI, PETX, and ultrasonic diagnostic imaging apparatus, and technologies thereof, early detection, preoperative planning, and therapeutic outcome evaluation of a malignant lesion has been efficiently performed.
On the other hand, factors, such as an increased high-resolution level and further finely-spliced images, require a diagnostician to treat a larger volume of image data, imposing a heavy burden on radiologists and clinicians. In recent years, to particularly reduce such a burden on radiologists and clinicians, the development of computer-aided diagnosis (CAD) apparatuses for assisting these diagnosticians to diagnose a lesion by digitizing a vast volume of medical image data has been advanced.
As for the aforementioned CAD apparatus, an apparatus for automatically detecting lesions, such as cancers, has been developed to minimize an oversight of any lesion on medical images by diagnosticians. Japanese Unexamined Patent Application Publication No. 2010-086449 discloses a lesion detection apparatus using a discriminator and a machine-learning method. The lesion detection apparatus disclosed in Japanese Unexamined Patent Application Publication No. 2010-086449, which has a control unit for generating the discriminator capable of determining the category (class) of input data, is characterized in that the control unit modifies the complexity of a probabilistic model for the discriminator, calculates the threshold for the information volume of the probabilistic model of interest, and uses a probabilistic model with minimum volume of information meeting the calculated threshold as the probabilistic model for the discriminator.